Anomaly detection data workflow for time series data

ABSTRACT

Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

BACKGROUND

Timeseries data processing is used in various industries for identifyingpatterns and anomalies. For example, in the cybersecurity industrytimeseries data may be used to identify anomalies that correspond tosecurity threats or security breaches, which may be vital to manyenterprises. In another example, timeseries data is used to determineanomalies in weather patterns. In yet another example, timeseriesprocessing is used by financial institutions to detect fraudulentactivity or other activity that is out of the ordinary (e.g., anomalous)and apply targeted defenses. Some anomaly detection scenarios sufferfrom inaccurate results, for example, cases where many/too many falsepositive results are detected (e.g., an anomaly has been detected, butno anomaly exists at the detection time). More recently, enterprisesstarted using machine learning to build analysis models and to processtimeseries data in order to more efficiently and accurately identifyanomalies.

SUMMARY

There are several types of machine learning models that can be trainedto process specific types of timeseries data efficiently and accurately.For example, some machine learning models are better suited fortimeseries data exhibiting a trend while other machine learning modelsare better suited for timeseries data exhibiting heteroskedasticattributes. Selecting a proper model for identifying anomalies, or asuitable transformation applied to the data based on the timeseriestrait (e.g., trend smoothing) in a specific set of timeseries data maygreatly improve accuracy and efficiency of anomaly detection.

Currently, two approaches are used to select a proper model. The firstapproach is a manual approach where a person explores the timeseriesdata and determines the best model to use. The second approach isreferred to as a brute force approach where the timeseries data is runthrough every model and a person determines which model has the bestresults. Both approaches are inefficient and/or lead to inaccurateresults. For example, a person exploring timeseries data is both a verytime consuming and resource consuming process. In many cases, this isunacceptable because a fast or near real-time response is required toprocess the data. The brute force method requires a large amount ofcomputing resources and time to run the data through each availablemodel and may result in a poorly selective rule. For example, if twentydifferent models are available, the time to run the data through eachmodel and the human factor of analyzing the data makes the processunacceptable for many applications (e.g., cybersecurity applications orfraud detection applications).

One application where automatic model selection is especially relevantis providing anomaly detection as a service. For example, an applicationprogramming interface may be provided to a user that enables a user tosubmit a timeseries dataset to an anomaly detection system together withsome metadata that may include a timeseries signal value indicating thetype of data (e.g., temperature readings) and in response, the anomalydetection system may output back to the user anomalies that have beenlocated within the data.

Methods and systems are described herein for automatically selecting anappropriate model for processing timeseries data. An appropriate modelmay be selected by a model selection system based on a temporal traitassociated with a timeseries dataset. For example, the model selectionsystem may receive a timeseries dataset that includes timestamps andcorresponding values. The timeseries dataset may include datarepresenting a frequency of various security events from differentcomputer systems. In another example, the timeseries dataset may be aset of temperatures at various times of the day, week, month, or year indifferent locations.

The model selection system may determine a temporal trait associatedwith the timeseries dataset. A temporal trait may identify a patternwithin the timeseries dataset, the pattern indicating a trend,heteroskedasticity, seasonality, serial correlation, or an approximateconstant. In some embodiments the temporal trait may identify anothercharacteristic of the timeseries dataset. The model selection system mayselect, based on the temporal trait, an anomaly detection model fordetecting anomalies in the timeseries dataset. The model selectionsystem may select a model from a multitude of models such that eachmodel is matched with a corresponding temporal trait. For example, themodel selection system may store a table with entries where a particulartemporal trait matches a particular model.

When the model selection system selects an appropriate model, the modelselection system may adjust one or more model parameters so that themodel is enabled to detect anomalies with better accuracy and/or speed.Thus, the model selection system may determine, a timeseries signalassociated with the timeseries dataset. For example, the timeseriessignal may be a type (e.g., temperature measurements) and frequency(e.g., hourly, daily) of the data entries within the timeseries dataset.That information may be received with the timeseries dataset. Thus,adjusting model execution based on the timeseries signal may improveaccuracy and/or speed of execution. For example, if the timeseriessignal indicates that the data within the timeseries dataset includestemperature measurements, the execution of the model may be adjusted forthose measurements. In another example, if the data in the timeseriesdataset includes security log codes, the execution of the model may beadjusted differently for that data than for the temperaturemeasurements.

Thus, the model selection system may select one or more executionparameters based on the timeseries signal. For example, the anomalydetection model may be configured to use different settings depending onthe model execution parameter. If the model execution parameterindicates that temperature measurements will be used to search foranomalies, the model may adjust processing and/or output, based on thatexecution parameter, for temperature measurements.

In some embodiments, the model selection system may perform parameteradjustment/selection by creating a grid of multiple sets of parametervalues, and fitting the selected model for each element on the grid/set.Each element in the grid may include a unique set of parameter values.The model selection system may compute a distribution of the anomaliesresulting from each fitted instance and set a threshold to be one sigmadeviation (assuming the distribution is always a gaussian distribution).The model selection system may then select those parameter values withthe highest probability/score.

The model selection system may input the timeseries dataset and themodel execution parameter into the anomaly detection model, and receive,from the anomaly detection model, one or more anomalies associated withthe timeseries dataset. Each detected anomaly may include correspondingtimeseries data from the timeseries dataset. In some embodiments, eachdetected anomaly may have a probability value associated with theanomaly and the model selection system may include a probabilitythreshold for determining which output values from the anomaly detectionmodel are anomalies. In some embodiments, the model selection system mayreceive, from the anomaly detection model, one or more timestampsassociated with each anomaly. Each timestamp may be associated with adetected anomaly.

In some embodiments, prior to inputting the timeseries dataset into ananomaly detection model, the model selection system may perform one ormore preprocessing operations on the timeseries dataset. For example, isthe timeseries dataset has a trend, the model selection system may applya smoothing function to the dataset. In another example, if the data isseasonal, the model selection system may detect the period using thespectral/residual algorithm.

The model selection system may generate an alert or multiple alertsbased on the one or more anomalies and transmit the alert or multiplealerts to an alert processing system. Each alert may include a timestampassociated with the anomaly and/or data describing the anomaly.

In some embodiments, the model selection system may perform thefollowing actions when selecting an anomaly detection model. The modelselection system may compare the temporal trait with stored temporaltraits (e.g., stored in a database). For example, each temporal traitmay be stored in association with a corresponding anomaly detectionmodel (e.g., in a database table) and the model selection system maycompare the temporal trait identified for the timeseries dataset withstored temporal traits.

The model selection system may identify, based on the comparing, amatching temporal trait of the plurality of temporal traits. Forexample, if the temporal traits are stored in a database table with acorresponding anomaly detection model, the model selection system mayidentify the table entry that matches the identified temporal trait. Themodel selection system may select the anomaly detection model based onthe anomaly detection model corresponding to the matching temporaltrait. For example, the model selection system may access the tableentry that matches the identified temporal trait and retrieve from thetable entry the corresponding anomaly detection model.

Another way to improve anomaly detection is to process a multitude oftimeseries datasets for a time period (e.g., 90 days) to detectanomalies from those timeseries datasets and then correlate thosedetected anomalies by generating an anomaly timeseries dataset andidentifying anomalies within the anomaly timeseries dataset. Forexample, computer systems for a computing environment may each generatethousands of events daily. When each dataset is run through an anomalydetection model, the resulting number of detected anomalies may be inthe magnitude of hundreds, with those anomalies not necessarilyindicating an issue. For a person to review each anomaly may be timeconsuming and inefficient. Thus, to focus on anomalous activity withinthe sets, the system may generate a timeseries dataset of the detectedanomalies and execute anomaly detection on that dataset. Any detectedanomalies from the anomalies timeseries dataset may indicate to a userthat there is anomalous activity that needs to be investigated.

Thus, in some embodiments an anomaly detection system may receive aplurality of timeseries datasets. Each timeseries dataset may include atimestamp and a corresponding value. The data within each timeseriesdataset is arranged chronologically using the timestamps. Timeseriesdatasets may include the same type of data. For example, all datasetsmay include event log data from different computing systems within acomputing environment.

In some embodiments, timeseries datasets may include different types ofdata. For example, one timeseries dataset may include event log datafrom computing systems and another timeseries dataset may includetemperature data measured around those computing systems. Thus, thetimeseries datasets may include a first dataset having a first type ofdata and a second dataset having a second type of data. The datasetswith different types of data may be input into different anomalydetection models. Thus, the anomaly detection system may select, basedon the first type of data, a first anomaly detection model for the firstdataset. For example, the anomaly detection system may select an anomalydetection model suited for processing event log data. The anomalydetection system may select, based on the second type of data, a secondanomaly detection model for the second dataset. For example, the anomalydetection system may select an anomaly detection model suited forprocessing temperature data for that type of data.

In some embodiments, the anomaly detection system may input eachtimeseries dataset into an anomaly detection model to obtain a pluralityof sets of timestamps. Each set of timestamps may represent an anomalydetected within a corresponding timeseries dataset. For example, theoutput of the anomaly detection model may be a timestamp and aprobability or a score that the particular timestamp is associated witha value indicating an anomaly. For each timeseries dataset there may bemultiple anomalies, or no anomalies detected. For example, if a thousandtimeseries datasets are input into an anomaly detection model (or one ormore different anomaly detection models) the output for each dataset mayinclude one or more timestamps and a probability that each timestamp isassociated with an anomalous value. Thus, the anomaly detection systemmay receive, from one or more anomaly detection models, sets oftimestamps. Each set of timestamps may include one or more timestampsrepresenting one or more anomalies detected within a correspondingtimeseries dataset.

In some embodiments, the anomaly detection system may combine timestampswithin the plurality of sets of timestamps into an anomaly dataset andsort the anomaly dataset into a chronologically ordered dataset. Forexample, the anomaly detection model may select a first set oftimestamps corresponding to anomalies detected in that particular set,and store, in a data structure, the timestamps of the first set in achronological order. The anomaly detection system may select other setsof timestamps and add each timestamp from the one or more sets into thedata structure. After adding each timestamp from the other sets to thedata structure, results in the data structure including timestamps fromboth the first set and the one or more sets arranged in thechronological order. In some embodiments, the anomaly detection modelmay sort and combine the timestamps from the different timeseriesdatasets into a chronological ordered timeseries as part of theaggregation function and avoid the separate sorting and combining steps.

The anomaly detection system may then aggregate, based on a timeinterval, the chronologically ordered dataset into an anomaly timeseriesdataset. The anomaly timeseries dataset may include timestamps and acorresponding number of anomalies detected during a corresponding timeinterval. In some embodiments, to aggregate the data, anomaly detectionsystem may retrieve the time interval (e.g., aggregation per hour), andretrieve, from the chronologically ordered dataset, a time associatedwith a first timestamp stored in a first position within thechronologically ordered dataset. That is, the anomaly detection systemmay select the earliest timestamp. The anomaly detection system maytraverse the chronologically ordered dataset until a second timestamp isreached. The second timestamp may be the last timestamp within the timeinterval associated with the first timestamp. For example, if theaggregation interval is one hour (e.g., hourly) and the first timestampindicates 2:11 PM, the anomaly detection system may retrieve thechronologically first timestamp and determine which timeslot thetimestamp belongs to (e.g., 2 PM to 3 PM) by adding one hour to thetimestamp and rounding down to the nearest hour and subtracting one hourand rounding up to the nearest hour, making the time interval 2 PM to 3PM.

The anomaly detection model may then traverse the chronologicallyordered dataset until a second timestamp is reached. The secondtimestamp may be the last timestamp within the time interval associatedwith the first timestamp. For example, the anomaly detection model maytraverse the data until the last timestamp is reached (e.g., the lasttimestamp before 3 PM). When the second timestamp is reached, theanomaly detection model may generate an aggregated value based on allthe timestamps starting from the first timestamp and ending with thesecond timestamp. The aggregated value may represent a count ofanomalies detected starting with the first timestamp and ending with thesecond timestamp. For example, if there are ten timestamps that aretraversed between 2 PM and 3 PM, the anomaly detection system may storethe value ten in association with the time interval of 2 PM-3 PM.

In some embodiments, the anomaly detection system may aggregate thevalues based on different characteristics of the data within the timeinterval. For example, if the data includes temperatures, the anomalydetection system may determine the mean temperature during the timeinterval and use that value as the aggregated valued for that timeinterval. In some embodiments, the anomaly detection system mayaggregate the data by summing up the values corresponding to thedifferent data points.

When the data has been aggregated, the anomaly detection model may inputthe anomaly timeseries dataset into an anomaly detection model to obtainone or more anomalies detected by the anomaly detection model. Forexample, the anomaly detection system may input the anomaly timeseriesdataset into an anomaly detection model and receive the output thatincludes one or more timestamps and a probability that the particulartimestamp is associated with an anomaly. In some embodiments, theanomaly detection system may determine which timestamps are associatedwith anomalies based on a threshold probability or score value. That is,if the associated probability or score is higher than the threshold, theanomaly detection system may identify a particular timestamp as ananomaly. In another example, the anomaly detection system may removefrom the one or more anomalies those anomalies that do not meet theanomaly confidence threshold. Once one or more anomalies have beenidentified, the anomaly detection system may generate one or more alertsbased on the one or more anomalies, and transmit the one or more alertsto an alert processing system.

Another way to improve anomaly detection is to divide a dataset intomultiple datasets based on a type of anomaly detection requested. Forexample, the anomaly detection system may receive a request to detectanomalous activity with the request including a data attribute. Forexample, the anomaly detection system may receive a request to detectanomalous activity in event log data that has been received from amultitude of computing devices (e.g., computer systems, routers,switches, and/or other computing devices). The data may be stored in atable and organized based on each event. In one example, the request mayinclude a username, so that the anomaly detection system may break downthe data into datasets based on a username. In another example, therequest may include a computing device name as the dividing field. Thus,the anomaly detection system may divide the data into timeseriesdatasets corresponding to each computing device. In another scenario,the data may be temperature data or weather data and the dividing fieldmay be a location (e.g., county, town, etc.). In yet another scenariothe data may be fraud detection data with the client identifier being adividing field. A person skilled in the art would understand that otherexamples with other types of data are contemplated by this disclosure.

In addition to receiving the request, the anomaly detection system mayreceive a dataset that includes event data for events (e.g., computingsystem events or other types of events, weather events, client activityevents, or another suitable set of events). The event data may include aplurality of fields including a timestamp field, a value field, and aplurality of attribute fields.

In some embodiments, the anomaly detection system may compare the dataattribute with each attribute field, and determine, based on thecomparing, a dividing attribute for the dataset. The comparison may be atextual comparison. As mentioned above, the dividing attribute may be ausername, system name, county, town, client identifier and/or anothersuitable field. A person skilled in the art would understand that thedividing attribute may be a combination of fields (e.g., username andsystem name).

The anomaly detection system may divide, based on the dividingattribute, the dataset into multiple datasets. For example, if thedividing attribute is a computer system, the anomaly detection systemmay generate a dataset for each computer system. In another example, ifthe dividing attribute is a username, the anomaly detection system maygenerate a dataset for each username found in the received dataset.

The anomaly detection system may then aggregate, based on a timeinterval, the datasets into timeseries datasets. For example, theanomaly detection system may aggregate data into hourly intervals andhave one entry per hour with a corresponding number of events. In someembodiments, the anomaly detection system may sort each timeseriesdataset into a chronological order based on timestamps and aggregatethose data points into hourly values. For example, there may be fifteenevents between 2 PM and 3 PM. Thus, the anomaly detection system maygenerate one timestamp (e.g., 2 PM) and add a corresponding value offifteen in association with that timestamp.

In some embodiments, the anomaly detection system may aggregate thevalues based on different characteristics of the data within the timeinterval. For example, if the data includes temperatures, the anomalydetection system may determine the mean temperature during the timeinterval and use that value as the aggregated valued for that timeinterval. In some embodiments, the anomaly detection system mayaggregate the data by summing up the values corresponding to thedifferent data points.

When the timeseries datasets are ready (or as each timeseries dataset isready) the anomaly detection system may input the timeseries datasetsinto one or more anomaly detection models to obtain sets of anomalies.For example, the anomaly detection system may use one or more differentanomaly detection models for the different datasets. In someembodiments, the anomaly detection system may use one anomaly detectionmodel, while in other embodiments, the anomaly detection system may usemultiple models. For example, the anomaly detection system may usemultiple models for different data types. Each anomaly detection modelmay output none, one or more anomalies for each timeseries dataset.

When the anomalies are received from the one or more anomaly detectionmodels, the anomaly detection system may generate an anomaly timeseriesdataset from the sets of anomalies. In some embodiments, the anomalydetection system may combine and sort into a chronological order all theanomalies in the sets of anomalies. Then the anomaly detection model mayaggregate the resulting dataset. Aggregation may include a number ofactions. For example, the anomaly detection system may aggregate over aspecific aggregation time interval (e.g., one minute, one hour, one day,or another suitable aggregation interval). For example, the anomalydetection system may retrieve an aggregation time interval, which may bestored in memory and/or in physical storage. The anomaly detectionsystem may also retrieve, from a chronologically ordered dataset, a timeassociated with a first timestamp stored in a first position within thechronologically ordered dataset. That is, the timestamp may be theearliest timestamp in the dataset. For example, the earliest timestampmay be 1-1-2021 14:03:00 (2:03 PM on Jan. 1, 2021). The anomalydetection system may then traverse the chronologically ordered datasetuntil a second timestamp is reached. The second timestamp may be thelast timestamp within the time interval associated with the firsttimestamp. For example, the time interval may be between 2 PM and 3 PM,thus the second timestamp may be 1-1-2021 14:59:00 (2:59 PM on Jan. 1,2021).

The anomaly detection system may then generate an aggregated value basedon all the timestamps starting from the first timestamp and ending withthe second timestamp. The aggregated value may represent a count ofanomalies detected starting with the first timestamp and ending with thesecond timestamp. For example, if there are ten timestamps between thefirst timestamp and the second timestamp, the anomaly detection systemmay aggregate those timestamps into a value of twelve for the timeslot(the ten timestamps plus the first timestamp and the second timestamp).

When the anomaly timeseries dataset is ready, the anomaly detectionsystem may input the anomaly timeseries dataset into an anomalydetection model to obtain one or more anomalies. In some embodiments,the anomaly detection system may use the model selection system asdescribed above and in the model selection section of this disclosure toselect an appropriate model for anomaly detection, based on a temporaltrait of the timeseries dataset. That is, the anomaly detection systemmay obtain anomalous behavior for anomalies detected in the previouslydiscussed datasets. The anomaly detection system may then generate oneor more alerts based on the one or more anomalies and transmit the oneor more alerts to an alert processing system. For example, the anomalydetection system may generate one alert per an anomaly, combine alertsfor several anomalies, and/or generate one alert and transmit it to theuser in response to the user request. In some embodiments, the alert maybe a message or output provided to a user. For example, the message maybe generated indicating a result of the anomaly detection, which can besent to the user (e.g., at a user's terminal, smart phone, or anotherdevice). In some embodiments, the alert is presented to the user on thesame device that the anomaly detection system is executed.

Various other aspects, features and advantages of the system will beapparent through the detailed description and the drawings attachedhereto. It is also to be understood that both the foregoing generaldescription and the following detailed description are examples and notrestrictive of the scope of the disclosure. As used in the specificationand in the claims, the singular forms of “a,” “an,” and “the” includeplural referents unless the context clearly dictates otherwise. Inaddition, as used in the specification and the claims, the term “or”means “and/or” unless the context clearly dictates otherwise.Additionally, as used in the specification “a portion,” refers to a partof, or the entirety of (i.e., the entire portion), a given item (e.g.,data) unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative system for selecting an anomaly detectionmodel, in accordance with one or more embodiments of this disclosure.

FIG. 2 illustrates an exemplary timeseries dataset that includestimestamps and corresponding number of security events, in accordancewith one or more embodiments of this disclosure.

FIG. 3 illustrates actions for determining a temporal trait associatedwith a timeseries dataset, in accordance with one or more embodiments.

FIG. 4 illustrates a table with temporal traits matching differentanomaly detection models, in accordance with one or more embodiments.

FIG. 5 illustrates a table with different timeseries signals andassociated model execution parameters, in accordance with one or moreembodiments.

FIG. 6 illustrates an exemplary anomaly detection model based on machinelearning, in accordance with one or more embodiments.

FIG. 7 illustrates an exemplary process for detecting anomalies intimeseries datasets based on temporal traits, in accordance with one ormore embodiments.

FIG. 8 illustrates another exemplary process for detecting anomalies intimeseries datasets based on temporal traits, in accordance with one ormore embodiments.

FIG. 9 shows an illustrative system for correlating events based onanomalies occurring within a given time interval across multipletimeseries datasets, in accordance with one or more embodiments of thisdisclosure.

FIG. 10 illustrates exemplary sets of timestamps from different datasetsrepresenting times of detected anomalies, in accordance with one or moreembodiments of this disclosure.

FIG. 11 illustrates a table that includes timeslots and correspondingnumber of anomalies, in accordance with one or more embodiments of thisdisclosure.

FIG. 12 illustrates an exemplary process for correlating events based onanomalies occurring within a given time interval across multipletimeseries datasets, in accordance with one or more embodiments of thisdisclosure.

FIG. 13 illustrates another exemplary process for correlating eventsbased on anomalies occurring within a given time interval acrossmultiple timeseries datasets, in accordance with one or more embodimentsof this disclosure.

FIG. 14 illustrates fields of a dataset of system log entries, inaccordance with one or more embodiments of this disclosure.

FIG. 15 illustrates an exemplary process for improving detection ofanomalous activity, in accordance with one or more embodiments of thisdisclosure.

FIG. 16 illustrates another exemplary process for improving detection ofanomalous activity, in accordance with one or more embodiments of thisdisclosure.

FIG. 17 illustrates a computing system that may perform actions, inaccordance with some embodiments of this disclosure.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the disclosed embodiments. It will be appreciated,however, by those having skill in the art, that the embodiments may bepracticed without these specific details or with an equivalentarrangement. In other cases, well-known models and devices are shown inblock diagram form in order to avoid unnecessarily obscuring thedisclosed embodiments. It should also be noted that the methods andsystems disclosed herein are also suitable for applications unrelated tosource code programming.

Model Selection

FIG. 1 illustrates system 100 for selecting an anomaly detection model.System 100 includes model selection system 102, data node 104 and alertprocessing systems 106 a-106 n connected by network 150. Model selectionsystem 102 may execute instructions for selecting an appropriate anomalydetection model. Model selection system 102 may include softwarehardware or the combination of the two. For example, model selectionsystem 102 may be a physical server or a virtual server that is runningon top of a physical computer system. Data node 104 may store timeseriesdata (e.g., in one or more databases). Data node 104 may includesoftware hardware or the combination of the two. For example, data node104 may be a physical server or a virtual server that is running on topof a physical computer system. Alert processing systems 106 a-106 n mayprocess alerts generated based on detected anomalies. Alert processingsystems 106 a-106 n may include software, hardware, or a combination ofthe two. For example, each alert processing system may be a physicalserver or a virtual server that is running on top of a physical computersystem. Model selection system 102, data node 104 and alert processingsystems 106 a-106 n may reside on the same hardware servers or differenthardware servers. In some embodiments, these components may reside onvirtual servers. Network 150 may be a local area network, a wide areanetwork (e.g., the Internet), or a combination of the two.

Model selection system 102 may be configured to receive a timeseriesdataset, for example, from data node 104. The timeseries dataset mayinclude values and corresponding timestamps. For example, FIG. 2illustrates an exemplary timeseries dataset 200 that includes timestampsand corresponding number of security events. Column 202 of FIG. 2includes timeslots while column 204 includes a corresponding number ofsecurity events. The number of security events in column 204 have beenaggregated on an hourly basis. Thus, for example, each of the securityevents in row 206 may have been recorded between the timestamp of row206 and the timestamp of row 208. Therefore, in some embodiments, datanode 104 or model selection system 102 may aggregate received data intoa timeseries dataset. In other examples, timeseries datasets may includecommunications records (e.g., from financial transactions), temperatures(e.g., daily, weekly, hourly, etc.), or other suitable data.

Model selection system 102 may receive the timeseries dataset usingcommunication subsystem 112. Communication subsystem 112 may includesoftware components, hardware components, or a combination of both. Forexample, communication subsystem 112 may include a network card (e.g., awireless network card and/or a wired network card) that is coupled withsoftware to drive the card. When the timeseries dataset is received,communication subsystem 112 may pass the received timeseries dataset totemporal trait detection subsystem 114.

Temporal trait detection subsystem 114 may determine a temporal traitassociated with the timeseries dataset. As referred to herein, thetemporal trait identifies a pattern within the timeseries dataset.Temporal trait detection subsystem 114 may execute a function fordetermining a temporal trait of a timeseries dataset. The temporaltraits may include a determination whether the data has a trend,heteroskedasticity, seasonality, an approximate constant, or anothersuitable temporal trait.

To make the determination, temporal trait detection subsystem 114 mayexecute a detection process that includes a number of actions. FIG. 3illustrates actions for determining a temporal trait associated with atimeseries dataset. At 302, temporal trait detection subsystem 114 maydetermine whether the timeseries dataset includes data that is constant.Temporal trait detection subsystem may perform this action by analyzingthe values for each timestamp within the timeseries dataset to determinewhether the values are substantially constant. If the data is determinedto be constant, process 300 moves to 304, where temporal trait detectionsubsystem 114 may determine an approximated constant.

If temporal trait detection subsystem 114 determines that the timeseriesdataset does not include data that is constant, process 300 moves to306. At 306, temporal trait detection subsystem 114 may determinewhether the timeseries dataset is serially correlated. This is sometimesreferred to as time correlation. For example, temporal trait detectionsubsystem 114 may execute an adapted Durbin-Watson test that includesdetermining whether residuals of a linear fit are autocorrelated. If thetimeseries dataset is not serially correlated, process 300 moves to 304,where temporal trait detection subsystem 114 may determine anapproximate constant for the timeseries dataset. If the timeseriesdataset is serially correlated, process 300 moves to perform actions308, 310, and 312.

At 308, temporal trait detection subsystem 114 determines whether thetimeseries data has a trend. Temporal trait detection subsystem 114 mayexecute a combination of Dickey-Fuller andKwiatkowski-Phillips-Schmidt-Shin (KPSS) for stationary andtrend-stationary tests to determine whether the series data exhibits alinear trend (e.g., whether the mean changes linearly). As a result ofexecuting these tests, temporal trait detection subsystem 114 maydetermine whether the series data has trend.

At 310, temporal trait detection subsystem 114 determines whethertimeseries data is heteroskedastic. To determine whether the timeseriesdata is heteroskedastic, temporal trait detection subsystem 114 mayexecute constant variance on the residuals of a linear fit (sometimesreferred to as homoscedasticity). At 312, temporal trait detectionsubsystem 114 determines whether the timeseries data is seasonal. Forexample, temporal trait detection subsystem 114 may execute aFisher-G-Test which determines whether the series exhibits a significantcyclic pattern. In some embodiments, an asymptotic solution to theFisher-G-Test may be adopted.

In some embodiments, temporal trait detection subsystem 114 may generatea schema for the timeseries dataset. The schema may include a flagindicating the temporal trait. For example, the schema may be in a formof an XML file or another data structure that may be associated with thetimeseries dataset. The flag may be textual data indicating the temporaltrait or may be a numeric or an alphanumeric string that maps todifferent temporal traits and/or anomaly detection models. In someembodiments, the schema may include a flag indicating the type of datathat is included in the timeseries dataset sometimes referred to astimeseries signal.

When temporal trait detection subsystem 114 determines a temporal traitin the timeseries dataset, the temporal trait detection subsystem maypass the temporal trait to model matching subsystem 116. Model matchingsubsystem 116 may select, based on the temporal trait, from a pluralityof anomaly detection models, an anomaly detection model for detectinganomalies in the timeseries dataset. That is, each model of theplurality of anomaly detection models may be matched with acorresponding temporal trait. FIG. 4 illustrates a table with temporaltraits matching different anomaly detection models. Rows 402 illustratesthat Model 1 will be used for timeseries datasets that have trendingdata while row 404 illustrates that Model 2 will be used for timeseriesdatasets that exhibit heteroskedasticity.

In some embodiments, model matching subsystem 116 may perform thefollowing operations when selecting the anomaly detection model. Modelmatching subsystem 116 may compare the temporal trait with a pluralityof temporal traits. Each temporal trait may be stored in associationwith a corresponding anomaly detection model (e.g., as illustrated inFIG. 4). Model matching subsystem 116 may identify, based on thecomparing, a matching temporal trait. For example, model matchingsubsystem 116 may traverse the temporal trait column in table 400 untilthe determined temporal trait matches a temporal trait in the temporaltrait column. When model matching subsystem 116 identifies the correcttemporal trait, model matching subsystem 116 may select the anomalydetection model corresponding to the matching temporal trait. In oneexample, each temporal trait may have a corresponding machine learningmodel trained for detecting anomalies in timeseries datasets classifiedunder each temporal trait. In some embodiments, each model is trainedwith datasets classified to include a specific type of timeseries data.For example, a model to be used with trending datasets, is trained usingdatasets previously classified as having trend. In another example, amodel to be used with seasonal datasets, is trained using datasetspreviously classified as seasonal.

In some embodiments, a corresponding model may be trained for eachtemporal trait with appropriate data for that specific temporal trait.For example, a model may be trained with trending datasets to be used ontimeseries datasets that are determined to have trend. In anotherexample, a model may be trained for seasonal timeseries datasets usingdatasets that are labeled as seasonal. In some embodiments, differenttypes of anomaly detection models may be used for timeseries datasetsassociated with different traits. For example, for timeseries datasetshaving a seasonal temporal trait, the anomaly detection system may use aSpectral Residual model or a Seasonal Hybrid Extreme Studentized Deviate(SHESD) model. In another example, for timeseries datasets having anon-seasonal series serial correlation, model matching subsystem 116 mayuse an Exponential Moving Average (EMA) model and/or Holts-Wintersforecasting model. In yet another example, for timeseries datasets thatare non-serial correlated or constant (e.g., having constant datasignals), model matching subsystem 116 may use a Gaussian/KDE model oranother model (e.g., distribution or threshold model). In addition,model matching subsystem 116 may apply different transformation ortransformations during preprocessing based on the temporal trait and/orthe model used.

In some embodiments, model matching subsystem 116 may determine modelexecution based on timeseries signal. The model matching subsystem maydetermine a timeseries signal associated with the timeseries dataset.For example, if the data in the timeseries dataset is temperaturemeasurements (timeseries signal) model execution may be adjusted to fitthose measurements. If the data in the timeseries dataset indicates anumber of events (timeseries signal), model execution may be adjusted tofit that data. Thus, model matching subsystem may select the modelexecution parameter based on the timeseries signal. FIG. 5 illustrates atable with different timeseries signals and associated model executionparameters. For example, timeseries signal indicating temperaturemeasurements may correspond to Parameter 1 of model executionparameters. Timeseries signal for security log data (e.g., security logentries) may correspond to parameter 2. In some embodiments, modelmatching subsystem 116 may adjust thresholds for different statisticaltests/models such as a p-value for a seasonal test, a p-value for aserial-correlated test based on the timeseries signal. In someembodiments, model matching subsystem 116 may adjust general probabilitythresholds based on the timeseries signal. One or more of thoseparameters may be model execution parameters.

In some embodiments, model matching subsystem 116 may perform parameteradjustment/selection by creating a grid of multiple sets of parametervalues, and fitting the selected model for each element on the grid/set.Each element in the grid may include a unique set of parameter values.Model matching subsystem 116 may compute a distribution of the anomaliesresulting from each fitted instance and set a threshold to be one sigmadeviation (assuming the distribution is always a gaussian distribution).Model matching subsystem 116 may then select those parameter values withthe highest probability/score.

In some embodiments, model matching subsystem 116 may perform one ormore preprocessing operations on the timeseries dataset. For example, isthe timeseries dataset has a trend, the model selection system may applya smoothing function to the dataset. In another example, if the data isseasonal, the model selection system may detect the period using thespectral/residual algorithm. In yet another example, if the timeseriesdataset is approximately constant, model matching subsystem 116 mayperform Gaussian/KDE transformation.

Model matching subsystem 116 may input the timeseries dataset into theanomaly detection model, and receive, from the anomaly detection model,one or more anomalies associated with the timeseries dataset. In someembodiments, model matching subsystem 116 may input the model executionparameter into the anomaly detection model so that the execution of themodel is modified, as discussed above. Model matching subsystem 116 mayreceive from the anomaly detection model timestamps and correspondingprobabilities or scores that indicate the likelihood that the timestampis associated with an anomaly. Model matching subsystem 116 may retrievea threshold value and compare the threshold value with each probabilityor score to identify the timestamps that are associated with anomalies.For example, the threshold value may be fifty percent, 0.5, or anothersuitable value. Any timestamp with a lower probability or score may beremoved from the set of anomalies.

FIG. 6 illustrates an exemplary anomaly detection model based on machinelearning. Machine learning model 602 (e.g., anomaly detection model) maytake input 604 (e.g., timeseries dataset) and may output timestamps 606(sometimes referred to as output parameters) corresponding to anomaliesdetected by the model. Timestamps 606 may be output together with aprobability that a particular timestamp corresponds to an anomalydetected in the timeseries dataset. Model matching subsystem 116 maypass each detected anomaly to alerting subsystem 118.

The output parameters may be fed back to the machine learning model asinput to train the machine learning model (e.g., alone or in conjunctionwith user indications of the accuracy of outputs, labels associated withthe inputs, or with other reference feedback information). The machinelearning model may update its configurations (e.g., weights, biases, orother parameters) based on the assessment of its prediction (e.g., of aninformation source) and reference feedback information (e.g., userindication of accuracy, reference labels, or other information).Connection weights may be adjusted, for example, if the machine learningmodel is a neural network, to reconcile differences between the neuralnetwork's prediction and the reference feedback. One or more neurons ofthe neural network may require that their respective errors are sentbackward through the neural network to facilitate the update process(e.g., backpropagation of error). Updates to the connection weights may,for example, be reflective of the magnitude of error propagated backwardafter a forward pass has been completed. In this way, for example, themachine learning model may be trained to generate better predictions.

In some embodiments, the machine learning model may include anartificial neural network. In such embodiments, machine learning modelmay include an input layer and one or more hidden layers. Each neuralunit of the machine learning model may be connected with one or moreother neural units of the machine learning model. Such connections maybe enforcing or inhibitory in their effect on the activation state ofconnected neural units. Each individual neural unit may have a summationfunction which combines the values of all of its inputs together. Eachconnection (or the neural unit itself) may have a threshold functionthat a signal must surpass before it propagates to other neural units.The machine learning model may be self-learning and/or trained, ratherthan explicitly programmed, and may perform significantly better incertain areas of problem solving, as compared to computer programs thatdo not use machine learning. During training, an output layer of themachine learning model may correspond to a classification of machinelearning model and an input known to correspond to that classificationmay be input into an input layer of machine learning model duringtraining. During testing, an input without a known classification may beinput into the input layer, and a determined classification may beoutput.

A machine learning model may include embedding layers at which eachfeature of a vector is converted into a dense vector representation.These dense vector representations for each feature may be pooled at oneor more subsequent layers to convert the set of embedding vectors into asingle vector.

The machine learning model may be structured as a factorization machinemodel. The machine learning model may be a non-linear model and/orsupervised learning model that can perform classification and/orregression. For example, the machine learning model may be ageneral-purpose supervised learning algorithm that the system uses forboth classification and regression tasks. Alternatively, the machinelearning model may include a Bayesian model configured to performvariational inference on a graph and/or vector.

In some embodiments, machine learning model 602 may be trained toperform anomaly detection using one or more supervised techniques. As anexample, a training dataset including entries labeled with “anomalous,”“normal,” or one or more other labels or label sets may be obtained andused to train machine learning model 602. In one use case, an entrywithout an anomaly may be labeled as “nominal,” while an entry includinganomalous data may be labeled “anomaly.” In another use case, a Booleanmay be used to label anomalous entries versus normal entries such that a“True” label represents an entry including anomalous value(s), and a“False” label represents an entry without an anomalous value.

In some embodiments, machine learning model 602 may be trained toperform anomaly detection using one or more unsupervised techniques. Forexample, an isolation forest algorithm or other decision tree algorithmmay be used to configure machine learning model 602 based on a trainingdataset in an unsupervised manner. In one use case, an isolation orother decision tree (e.g., corresponding to machine learning model 602)may be trained or generated by selecting one or more features from thetraining dataset (e.g., selecting one or more parameters of the trainingdataset) and randomly selecting one or more values for a selectedfeature for splitting the data of that feature (e.g., randomly selectinga value between maximum and minimum values of that feature). In thisway, for example, the decision tree will have a high likelihood ofshorter paths in decision trees for anomalous data points, therebyidentifying data points corresponding to anomalous data.

Alerting subsystem 118 may generate an alert based on the one or moreanomalies. For example, alerting subsystem 118 may generate one alertfor each detected anomaly. The alert may include timeseries dataassociated with the timestamp for which the anomaly was detected. Insome embodiments, alerting subsystem 118 may generate one alert for alldetected anomalies and include the timeseries data associated with eachtimestamp. When the alert or alerts are generated, alerting subsystem118 may pass the alert or alerts to communication subsystem 112.

In some embodiments, alerting subsystem 118 may determine a destinationsystem/address for the alert to be sent to. Alerting subsystem 118 maydetermine that the timeseries dataset includes data associated with oneor more security logs from one or more computer systems. For example,alerting subsystem 118 may access timeseries dataset schema describedabove in relation to the time series signal. The schema may include anindication (e.g., a flag) that the time series dataset includes securitylog data. Alerting subsystem 118 may then retrieve one or more securityparameters associated with a security log entry corresponding to adetected anomaly. For example, the security parameters may be retrievedfrom the security event associated with the timestamp when the anomalywas detected. Those parameters may include the computer systemassociated with the event, username, action type, and other suitableparameters. In some embodiments, the parameters may indicate thealerting system that is appropriate for the event. Alerting subsystem118 may select, based on the one or more security parameters, an alertprocessing system corresponding to security log processing. For example,alerting subsystem 118 may select alert processing system 106 a forsecurity events.

A person skilled in the art would understand that alerting subsystem 118may target alerts to different alert processing systems 106 n based onthe type of timeseries data. In another example, alerting subsystem 118may determine that the timeseries dataset includes data associated witha plurality of communication records or transactions. For example, thoserecords may be user transactions using various transaction methods.Alerting subsystem 118 may retrieve one or more type of parametersassociated with a communication record corresponding to a detectedanomaly, and select, based on the one or more type parameters, the alertprocessing system (e.g., alert processing system 106 n) corresponding tocommunication record processing.

When the alerts have been created, alerting subsystem 118 may pass thealert or alerts to communication subsystem 112. Communication subsystem112 may transmit (e.g., via network 150) the alert or alerts to anappropriate alert processing system (e.g., alert processing system 106a).

FIG. 7 illustrates an exemplary process 700 for detecting anomalies intimeseries datasets based on temporal traits. At 702, model selectionsystem 102 receives a timeseries dataset, the timeseries datasetincluding a plurality of values for a plurality of timestamps. The modelselection system may receive the timeseries dataset from a database(e.g., a database residing on data node 104). Model selection system 102may include one or more processors, memory and other componentsdescribed above. At 704, model selection system 102 determines atemporal trait associated with the timeseries dataset. The temporaltrait may identify a pattern within the timeseries dataset, the patternindicating a trend, heteroskedasticity, seasonality, or an approximateconstant. Model selection system 102 may use one or more processors tomake the determination.

At 706, model selection system 102 selects an anomaly detection modelfor detecting anomalies in the timeseries dataset. Each model may bematched with a corresponding temporal trait. For example, modelselection system 102 may perform temporal trait analysis (e.g., usingone or more processors) and access a lookup table (e.g., illustrated inFIG. 4) to perform the selection. At 708, model selection system 102determines a timeseries signal associated with the timeseries dataset.Model selection system 102 may make the determination using one or moreprocessors and access table 500 of FIG. 5 to perform a lookup. At 710,model selection system 102 selects a model execution parameter based onthe timeseries signal. For example, model selection system 102 may useone or more processors to generate a data structure corresponding to theparameter such that the data structure may be used as input into ananomaly detection model. In some embodiments, if the timeseries data istemperature measurements, the model execution parameter may be selectedto indicate temperature data. If the timeseries data includes counts ofoccurrences, the model execution parameter may be selected to indicatecount of occurrences.

At 712, model selection system 102 inputs the timeseries dataset and themodel execution parameter into the anomaly detection model. For example,the anomaly detection model may be a machine learning model asillustrated in FIG. 6, which can reside in the model selection system oroutside of the model selection system. If the machine learning modelresides outside of the model selection system, the model selectionsystem may send (or transmit) the data to another system that hosts themachine learning model.

At 714, model selection system 102 receives, from the anomaly detectionmodel, one or more anomalies associated with the timeseries dataset. Forexample, the model selection system may receive from machine learningmodel 602 timestamps 606 that may include one or more anomalies. Theanomalies may be timestamps. In some embodiments, model selection system102 may receive probability/score information for every anomalyindicating how confident the machine learning model is that the anomalyhas been properly detected.

At 716, model selection system 102 generates one or more alerts based onthe one or more anomalies. Model selection system 102 may generate oneor more alerts for the anomalies (e.g., one alert per anomaly). At 718,model selection system 102 transmits the one or more alerts to one ormore alert processing systems.

FIG. 8 illustrates another exemplary process 800 for detecting anomaliesin timeseries datasets based on temporal traits, in accordance with oneor more embodiments. At 802, model selection system 102 receives atimeseries dataset. The timeseries dataset may include values foranomalies and corresponding timestamps. The model selection system mayreceive the timeseries dataset from a database (e.g., a databaseresiding on data node 104). Model selection system 102 may include oneor more processors, memory and other components described above.

At 804, model selection system 102 determines a temporal traitassociated with the timeseries dataset. The temporal trait may identifya pattern within the timeseries dataset. The pattern may indicate atrend, heteroskedasticity, seasonality, or an approximate constant.Model selection system 102 may use one or more processors to make thedetermination. At 806, model selection system 102 determines, based onthe temporal trait, an anomaly detection model for detecting anomaliesin the timeseries dataset. Each model may be matched with acorresponding temporal trait. For example, model selection system 102may perform temporal trait analysis (e.g., using one or more processors)and access a lookup table (e.g., illustrated in FIG. 4) to perform theselection.

At 808, model selection system 102 inputs the timeseries dataset intothe anomaly detection model. For example, the anomaly detection modelmay be a machine learning model as illustrated in FIG. 6, which canreside in the model selection system or outside of the model selectionsystem. If the machine learning model resides outside of the modelselection system, the model selection system may send (or transmit) thedata to another system that hosts the machine learning model.

At 810, model selection system 102 receives, from the anomaly detectionmodel, one or more anomalies associated with the timeseries dataset. Forexample, the model selection system may receive from machine learningmodel 602 timestamps 606 that may include one or more anomalies. Theanomalies may be timestamps. In some embodiments, model selection system102 may receive probability/score information for every anomalyindicating how confident the machine learning model is that the anomalyhas been properly detected. At 812, model selection system 102 generatesan alert based on the one or more anomalies. Model selection system 102may generate one or more alerts for the anomalies (e.g., one alert peranomaly).

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A method comprising: receiving a timeseries dataset, the timeseriesdataset comprising a plurality of values for a plurality of timestamps;determining a temporal trait associated with the timeseries dataset,wherein the temporal trait identifies a pattern within the timeseriesdataset; determining, based on the temporal trait, an anomaly detectionmodel of a plurality of anomaly detection models for detecting anomaliesin the timeseries dataset; inputting the timeseries dataset into theanomaly detection model; receiving, from the anomaly detection model,one or more anomalies associated with the timeseries dataset; andgenerating an alert based on the one or more anomalies.

2. The method of any of the preceding embodiments, wherein each anomalydetection model of the plurality of anomaly detection models is matchedwith a corresponding temporal trait.

3. The method of any of the preceding embodiments, wherein determiningthe temporal trait associated with the timeseries dataset includesexecuting one or more algorithms on the timeseries dataset, wherein theone or more algorithms identify the timeseries data as trending,heteroskedastic, seasonal, or constant.

4. The method of any of the preceding embodiments, wherein determining atemporal trait associated with the timeseries dataset comprisesgenerating a schema for the timeseries dataset, wherein the schemacomprises a flag indicating the temporal trait.

5. The method of any of the preceding embodiments, further comprising:selecting a model execution parameter based on a timeseries signalassociated with the timeseries dataset; and inputting the modelexecution parameter into the anomaly detection model.

6. The method of any of the preceding embodiments, further comprising:determining that the timeseries dataset comprises data associated withone or more security logs from one or more computer systems; retrievingone or more security parameters associated with a security log entrycorresponding to a detected anomaly; and selecting, based on the one ormore security parameters, an alert processing system corresponding tosecurity log processing.

7. The method of any of the preceding embodiments, further comprising:determining that the timeseries dataset comprises data associated with aplurality of communication records; retrieving one or more typeparameters associated with a communication record corresponding to adetected anomaly; and selecting, based on the one or more typeparameters, the alert processing system corresponding to communicationrecord processing.

8. The method of any of the preceding embodiments, wherein receiving,from the anomaly detection model, the one or more anomalies associatedwith the timeseries dataset comprises receiving one or more timestampsassociated with each anomaly.

9. A tangible, non-transitory, machine-readable medium storinginstructions that, when executed by a data processing apparatus, causethe data processing apparatus to perform operations comprising any ofthose in embodiments 1-8.

10. A system comprising: one or more processors; and memory storinginstructions that, when executed by the processors, cause the processorsto effectuate operations comprising any of those in embodiments 1-8.

11. A system comprising means for performing any of embodiments 1-8.

12. A system comprising cloud-based circuitry for performing any ofembodiments 1-8.

Anomaly Detection Data Workflow for Timeseries Data

FIG. 9 shows an illustrative system for correlating events based onanomalies occurring within a given time interval across multipletimeseries datasets. System 900 includes anomaly detection system 902,data node 904 and alert processing systems 906 a-906 n connected bynetwork 950. Anomaly detection system 902 may execute instructions forcorrelating events based on anomalies occurring within a given timeinterval across multiple timeseries datasets. Anomaly detection system902 may include software, hardware, or a combination of the two. Forexample, anomaly detection system 902 may be a physical server or avirtual server that is running on top of a physical computer system.Data node 904 may store timeseries data (e.g., in one or moredatabases). Data node 904 may include software hardware or thecombination of the two. For example, data node 904 may be a physicalserver or a virtual server that is running on top of a physical computersystem. Alert processing systems 906 a-906 n may process alertsgenerated based on detected anomalies. Alert processing systems 906a-906 n may include software, hardware, or a combination of the two. Forexample, each alert processing system may be a physical server or avirtual server that is running on top of a physical computer system.Anomaly detection system 902, data node 904 and alert processing systems906 a-906 n may reside on the same hardware servers or differenthardware servers. In some embodiments, these components may reside onvirtual servers. Network 950 may be a local area network, a wide areanetwork (e.g., the Internet) or a combination of the two.

Anomaly detection system 902 may be configured to receive, from one ormore anomaly detection models (e.g., located on data node 904), aplurality of sets of timestamps. Each set of the plurality of setstimestamps may include one or more timestamps representing one or moreanomalies detected within a corresponding timeseries dataset. Forexample, the timeseries dataset may include values and correspondingtimestamps. In some embodiments, one or more anomaly detection model maybe models as illustrated by FIG. 6 and the accompanying disclosure. Forexample, FIG. 10 illustrates table 1000 of exemplary sets of timestampsfrom different datasets representing times of detected anomalies. Column1002 of FIG. 10 includes timestamps corresponding to anomalies detectedin a particular dataset, while column 1004 includes timestampscorresponding to anomalies detected in another dataset. While FIG. 10illustrates anomalies based on data aggregated based on hourly basis, itis not a requirement that the data must be aggregated. Thus, in some ofthe datasets data may have looked like a dataset illustrated in FIG. 2,while other datasets may not have included aggregated data. Therefore,timestamps 1006 and 1008 may represent anomalies detected during thosetime intervals, based on aggregated data in a dataset.

Anomaly detection system 902 may receive the sets of timestamps usingcommunication subsystem 912. Communication subsystem 912 may includesoftware components, hardware components, or a combination of both. Forexample, communication subsystem 912 may include a network card (e.g., awireless network card and/or a wired network card) that is coupled withsoftware to drive the card. When the anomaly data is received,communication subsystem 912 may pass the received anomaly data todataset processing subsystem 914.

Dataset processing subsystem 914 may combine the timestamps within theplurality of sets of timestamps into an anomaly dataset, and sort theanomaly dataset into a chronologically ordered dataset. In someembodiments, the combination and sorting may be performed essentiallysimultaneously. For example, dataset processing subsystem 914 maygenerate a data structure for storing an anomaly timeseries dataset andthen iterate through each set of anomalies and place the anomalies intothe data structure in a chronological order. In some embodiments, thecombination and sorting operations may be performed separately. Forexample, dataset processing subsystem 914 may generate a data structureand copy the timestamps representing anomalies detected in the differentdatasets into the data structure. Dataset processing subsystem 914 maythen sort the timestamps into a chronological order.

In another example, dataset processing subsystem 914 may store, in adata structure (e.g., in memory), a first set of the plurality of setsof timestamps in a chronological order. For example, dataset processingsubsystem 914 may copy the data from the first set into a newlygenerated data structure. Dataset processing subsystem 914 may thenselect, each set of the plurality of sets of timestamps (e.g., inparallel or sequentially), and place each timestamp from the selectedset into the data structure in the chronological order. That is, datasetprocessing subsystem 914 may process each timestamp to place it into acorrect location in the data structure.

Dataset processing subsystem 914 may aggregate, based on a timeinterval, the chronologically ordered dataset into an anomaly timeseriesdataset. The anomaly timeseries dataset may include a plurality oftimestamps and a corresponding number of anomalies detected during acorresponding time interval. In some embodiments, dataset processingsubsystem 914 may perform the following operations to perform theaggregation. Dataset processing subsystem 914 may retrieve the timeinterval. For example, the time interval may be one minute, one hour,one day, or another suitable time interval. Dataset processing subsystem914 may retrieve the time interval from memory or from another suitablelocation.

Furthermore, dataset processing subsystem 914 may retrieve, from thechronologically ordered dataset, a time associated with a firsttimestamp stored in a first position within the chronologically ordereddataset. For example, dataset processing subsystem 914 may access a datastructure that stores the chronologically ordered dataset and retrievethe earliest entry (i.e., the first entry) in the dataset. In someembodiments, dataset processing subsystem 914 may determine a timeslotassociated with the first timestamp. For example, if the time intervalis one hour and the timestamp is 2021-01-01 01:11:00, dataset processingsubsystem 914 may determine that the timeslot for the entry is between 1PM and 2 PM on Jan. 1, 2021. The determination may be performed addingthe time interval to the timestamp and rounding down to the nearestinterval (e.g., nearest hour) and subtracting the time interval from thetimestamp and rounding up the timestamp to the nearest interval (e.g.,nearest hour).

Dataset processing subsystem 914 may then traverse the chronologicallyordered dataset until a second timestamp is reached. The secondtimestamp may be the last timestamp within a timeslot associated withthe first timestamp. For example, dataset processing subsystem 914 mayiterate through each timestamp and compare each timestamp with thetimeslot ending time. The process may proceed until a timestamp afterthe ending time is reached and then stop.

Dataset processing subsystem 914 may generate an aggregated value basedon all the timestamps starting from the first timestamp and ending witha last timestamp prior to the second timestamp. The aggregated value mayrepresent a count of anomalies detected starting with the firsttimestamp and ending with the second timestamp. For example, datasetprocessing subsystem 914 may add all the timestamps to arrive at theaggregated value for the specific timeslot.

In some embodiments, dataset processing subsystem 914 may aggregate thevalues based on different characteristics of the data within the timeinterval. For example, if the data includes temperatures, datasetprocessing subsystem 914 may determine the mean temperature during thetime interval and use that value as the aggregated value for that timeinterval. In some embodiments, dataset processing subsystem 914 mayaggregate the data by summing up the values corresponding to thedifferent data points.

As discussed above, dataset processing subsystem 914 may aggregate thedata based on an hourly interval. FIG. 11 illustrates table 1100 thatincludes timeslots and corresponding number of anomalies. Column 1102includes timeslots 1106 and 1108, while column 1104 includes a number ofanomalies detected in those timeslots.

Dataset processing subsystem 914 may input the anomaly timeseriesdataset into an anomaly detection model to obtain one or more anomaliesfrom the anomaly detection model. In some embodiments, datasetprocessing subsystem 914 may use the model selection system as describedabove and in the model selection section of this disclosure to select anappropriate model for anomaly detection, based on a temporal trait ofthe timeseries dataset. In some embodiments, the anomaly detection modelmay be a model as illustrated by FIG. 6 and the accompanying disclosure.For example, the output of the anomaly detection model may be timestampsor timeslots and for each a probability or a score that the particulartimestamp or timeslot is associated with a value indicating an anomaly.For each timeseries dataset there may be multiple anomalies, or noanomalies detected. In some embodiments, dataset processing subsystem914 may determine which timestamps are associated with anomalies basedon a threshold probability or score value. That is, if the associatedprobability or score is higher than the threshold, dataset processingsubsystem 914 may identify a particular timestamp as an anomaly. Forexample, dataset processing subsystem 914 may receive, from the anomalydetection model and based on the anomaly timeseries dataset, one or moreprobabilities corresponding to the one or more anomalies detected by theanomaly detection model, and retrieve an anomaly confidence threshold.The anomaly confidence value may be a threshold probability or athreshold score that determines whether a given probability correspondsto a positive detection of an anomaly.

Dataset processing subsystem 914 may remove from the one or moreanomalies those anomalies that do not meet the anomaly confidencethreshold.

Once one or more anomalies have been identified, dataset processingsubsystem 914 may pass the anomalies to alerting subsystem 918. Alertingsubsystem 918 may generate one or more alerts based on the one or moreanomalies, and transmit the one or more alerts to an alert processingsystem. For example, alerting subsystem 918 may generate one alert foreach detected anomaly. The alert may include timeseries data associatedwith the timestamp for which the anomaly was detected. In someembodiments, alerting subsystem 918 may generate one alert for all thedetected anomalies and include the timeseries data associated with eachtimestamp. When the alert or alerts are generated, alerting subsystem918 may pass the alert or alerts to communication subsystem 912.Communication subsystem 912 may transmit (e.g., via network 950) thealert or alerts to an appropriate alert processing system (e.g., alertprocessing system 106 a).

In some embodiments, dataset processing subsystem 914 may process amultitude of timeseries datasets to identify anomalies to place into theanomaly timeseries dataset. Dataset processing subsystem 914 may receivea plurality of timeseries datasets. Each timeseries dataset may includea plurality of values for a plurality of timestamps. In addition, theplurality of timeseries datasets may include a first dataset with afirst type of data and a second dataset with a second type of data. FIG.2 illustrates one example of a portion of a dataset.

Dataset processing subsystem 914 may input each of the plurality oftimeseries datasets into one or more anomaly detection models to obtaina plurality of sets of timestamps. Each set of the plurality of sets oftimestamps may include one or more timestamps representing one or moreanomalies detected within a corresponding timeseries dataset.

In some embodiments, dataset processing subsystem 914 may use differentanomaly detection models for different types of data when inputtingdatasets into the models. For example, if data within the dataset isstationary, dataset processing subsystem 914 may use one model. However,when the data within a dataset is trending, dataset processing subsystem914 may use another model. In some embodiments, dataset processingsubsystem 914 may use one anomaly detection model for temperature dataand another anomaly detection model for computer system event data. Forexample, dataset processing subsystem 914 may select, based on the firsttype of data, a first anomaly detection model for the first dataset andselect, and based on the second type of data, a second anomaly detectionmodel for the second dataset. Dataset processing subsystem 914 may inputthe first dataset into the first anomaly detection model and inputsecond dataset into the second anomaly detection model.

FIG. 12 illustrates an exemplary process for correlating events based onanomalies occurring within a given time interval across multipletimeseries datasets, in accordance with one or more embodiments of thisdisclosure. Process 1200 of FIG. 12 may be performed by anomalydetection system 902 of FIG. 9. At 1202, anomaly detection system 902receives, from one or more anomaly detection models, a plurality of setsof timestamps. The anomaly detection models may be hosted by data node904, and/or anomaly detection system 902. Thus, the sets of timestampsmay be received from data node 904 through network 950.

At 1204, anomaly detection system 902 combines timestamps within theplurality of sets of timestamps into an anomaly dataset. For example,anomaly detection system 902 may use one or more processors to performthe combining operation and store the resulting anomaly dataset in amemory and/or other storage. At 1206, anomaly detection system 902 sortsthe anomaly dataset into a chronologically ordered dataset. For example,anomaly detection system 902 may use one or more processors to performthe sorting operation and may store the result in memory and/or otherstorage.

At 1208, anomaly detection system 902 aggregates, based on a timeinterval, the chronologically ordered dataset into an anomaly timeseriesdataset. For example, anomaly detection system 902 may use one or moreprocessors to perform the aggregating operation and may store the resultin memory and/or other storage. At 1210, anomaly detection system 902inputs the anomaly timeseries dataset into an anomaly detection model toobtain one or more anomalies from the anomaly detection model. Forexample, the anomaly detection model may be hosted by anomaly detectionsystem 902, thus the input operation may be performed locally on thesystem. In another example, the anomaly detection model may be hosted bydata node 904. In this example, anomaly detection system 902 maytransmit the anomaly timeseries dataset to data node 904 and receivefrom data node 904 the detected anomalies. In some embodiments, one ormore anomaly detection model may be models as illustrated by FIG. 6 andthe accompanying disclosure.

At 1212, anomaly detection system 902 generates one or more alerts basedon the one or more anomalies. For example, anomaly detection system 902may use one or more processors to generate the alerts and may store thealerts in memory and/or other storage. At 1214, anomaly detection system902 transmits the one or more alerts to an alert processing system. Forexample, anomaly detection system 902 may transmit the alerts overnetwork 950 to alert processing system 906 a or any other alertprocessing systems 906 n.

FIG. 13 illustrates process 1300 for correlating events based onanomalies occurring within a given time interval across multipletimeseries datasets, in accordance with one or more embodiments of thisdisclosure. Process 1300 of FIG. 13 may be performed by anomalydetection system 902 of FIG. 9. At 1302, anomaly detection system 902receives a plurality of sets of timestamps. The anomaly detection modelsmay be hosted by data node 904, and/or anomaly detection system 902.Thus, the sets of timestamps may be received from data node 904 throughnetwork 950.

At 1304, anomaly detection system 902 combine timestamps within theplurality of sets of timestamps into a chronologically ordered datasetof anomalies. For example, anomaly detection system 902 may use one ormore processors to perform the combining operation and store theresulting anomaly dataset in a memory and/or other storage. In someembodiments, as part of the combining operation, anomaly detectionsystem 902 may sort the anomaly dataset into a chronologically ordereddataset. For example, anomaly detection system 902 may use one or moreprocessors to perform the sorting operation and may store the result inmemory and/or other storage.

At 1306, anomaly detection system 902 aggregates, based on a timeinterval, the chronologically ordered dataset into an anomaly timeseriesdataset. For example, anomaly detection system 902 may use one or moreprocessors to perform the aggregating operation and may store the resultin memory and/or other storage. At 1308, anomaly detection system 902inputs the anomaly timeseries dataset into an anomaly detection model toobtain one or more anomalies from the anomaly detection model. Forexample, the anomaly detection model may be hosted by anomaly detectionsystem 902, thus the input operation may be performed locally on thesystem. In another example, the anomaly detection model may be hosted bydata node 904. In this example, anomaly detection system 902 maytransmit the anomaly timeseries dataset to data node 904 and receivefrom data node 904 the detected anomalies. In some embodiments, one ormore anomaly detection model may be models as illustrated by FIG. 6 andthe accompanying disclosure.

At 1310, anomaly detection system 902 generates one or more alerts basedon the one or more anomalies. For example, anomaly detection system 902may use one or more processors to generate the alerts and may store thealerts in memory and/or other storage. At 1312, anomaly detection system902 transmits the one or more alerts to an alert processing system. Forexample, anomaly detection system 902 may transmit the alerts overnetwork 950 to alert processing system 906 a or any other alertprocessing systems 906 n.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A method comprising: receiving a plurality of sets of timestamps,wherein each set of the plurality of sets timestamps includes one ormore timestamps representing one or more anomalies detected within acorresponding timeseries dataset; combining timestamps within theplurality of sets of timestamps into a chronologically ordered datasetof anomalies; aggregating, based on a time interval, the chronologicallyordered dataset into an anomaly timeseries dataset; inputting theanomaly timeseries dataset into an anomaly detection model to obtain oneor more anomalies; generating one or more alerts based on the one ormore anomalies; and transmitting the one or more alerts to an alertprocessing system.

2. The method of any of the preceding embodiments, further comprising:receiving a plurality of timeseries datasets, wherein each timeseriesdataset comprises a plurality of values for a plurality of timestamps;and inputting each of the plurality of timeseries datasets into one ormore anomaly detection models.

3. The method of any of the preceding embodiments, wherein the pluralityof timeseries datasets includes a first dataset comprising a first typeof data and a second dataset comprising a second type of data.

4. The method of any of the preceding embodiments, wherein inputtingeach plurality of timeseries datasets into the one or more anomalydetection models comprises: selecting, based on the first type of data,a first anomaly detection model for the first dataset; selecting, basedon the second type of data, a second anomaly detection model for thesecond dataset; and inputting the first dataset into the first anomalydetection model and inputting second dataset into the second anomalydetection model.

5. The method of any of the preceding embodiments, wherein combining thetimestamps within the plurality of sets of timestamps into achronologically ordered dataset of anomalies comprises: storing, in adata structure, a first set of the plurality of sets of timestamps in achronological order; selecting, each set of the plurality of sets oftimestamps; and placing each timestamp from the selected set into thedata structure in the chronological order.

6. The method of any of the preceding embodiments, wherein aggregating,based on the time interval, the plurality of sets of timestamps into theanomaly timeseries dataset comprises: retrieving the time interval;retrieving, from the data structure, a time associated with a firsttimestamp stored in a first position within the data structure;traversing the data structure until a second timestamp is reached,wherein the second timestamp is the last timestamp within a timeslotassociated with the first timestamp; and generating an aggregated valuebased on all the timestamps between the first timestamp and the secondtimestamp, wherein the aggregated value represents a count of anomaliesdetected between the first timestamp and the second timestamp.

7. The method of any of the preceding embodiments, further comprising:receiving, from the anomaly detection model and based on the anomalytimeseries dataset, one or more probabilities corresponding to the oneor more anomalies detected by the anomaly detection model; retrieving ananomaly confidence threshold; and removing from the one or moreanomalies those anomalies that do not meet the anomaly confidencethreshold.

8. A tangible, non-transitory, machine-readable medium storinginstructions that, when executed by a data processing apparatus, causethe data processing apparatus to perform operations comprising any ofthose in embodiments 1-7.

9. A system comprising: one or more processors; and memory storinginstructions that, when executed by the processors, cause the processorsto effectuate operations comprising any of those in embodiments 1-7.

10. A system comprising means for performing any of embodiments 1-7.

11. A system comprising cloud-based circuitry for performing any ofembodiments 1-7.

Anomaly Detection in a Split Timeseries Dataset

Another way to improve anomaly detection is to divide a received datasetinto multiple datasets based on a type of anomaly detection requestedbefore submitting the datasets to anomaly detection models. For example,it may be desirable to execute anomaly detection on a dataset thatincludes a multitude of security log data from various computing systemswithin a datacenter. However, the dataset may be large and include alarge number of entries which may make it very difficult or impossibleto detect specific anomalies. For example, a user (e.g., a securityofficer) may want to execute anomaly detection based on user logininformation. That is, the security office may want to determine whetherone or more users have been accessing/copying/downloading data at ananomalous rate. In another example, a user may want to determine whetherone or more specific computing systems have had anomalouslogin/processing or other activity recorded. Thus, the user is enabledto provide an attribute for the anomalous activity detection that may beused to split/divide a dataset for appropriate anomaly detection.

System 900 of FIG. 9 may be used to perform the operations for improvingdetection of anomalous activity by dividing a dataset into multipledatasets based on the type of anomaly detection requested. Anomalydetection system 902 may receive (e.g., via communication subsystem 912)a request to detect anomalous activity. The request may include a dataattribute. For example, the data attribute may be a field or multiplefields in a dataset. Anomaly detection system 902 may retrieve the fieldfrom the request.

Anomaly detection system 902 may receive (e.g., via communicationsubsystem 912) a dataset including event data for a plurality of events.The event data may include a plurality of fields with a timestamp fieldand a plurality of attribute fields. FIG. 14 shows table 1400 thatillustrates fields of a dataset of system log entries. Table 1400includes rows 1402 that represent system log entries. Table 1400 mayinclude many entries (e.g., millions of entries) from one or multiplesystems. Field 1404 stores timestamps for the system events in thedataset, while field 1406 may store usernames and field 1408 may storean action associated with a particular entry. Each entry may includeother fields with other information. A person skilled in the art wouldunderstand that table 1400 may store different data (e.g., transactiondata, record data, or other type of data).

When the request and/or the dataset are received using communicationsubsystem 912, that data may be passed to dataset processing subsystem914. Anomaly detection system 902 (e.g., using dataset processingsubsystem 914) may compare the data attribute with each field of theplurality of attribute fields. For example, dataset processing subsystem914 may iterate through each field name in the dataset and compare thedata attribute with each field to identify a field that matches therequest. In some embodiments, the data attribute may correspond tomultiple fields. For example, FIG. 14 includes a user field (e.g., field1406) and an action field (e.g., field 1408), thus the attribute may beunique combinations of user and action fields.

In some embodiments, anomaly detection system 902 may receive thedataset and identify the different fields in the dataset. The anomalydetection system may generate an interface that enables a user to selectone or more fields detected in the dataset. The interface may bepresented to the user through, for example, a webpage, an application,or another suitable method. When the user selection is received, thedividing attribute may be determined by comparing the received one ormore selections with all the field names.

Thus, anomaly detection system 902 may determine (e.g., via datasetprocessing subsystem 914), based on the comparing, a dividing attributefor the dataset. To continue with the example in FIG. 14, the dividingattribute may be a user, a system, or any other suitable attribute. Insome embodiments, the dividing attribute may be a combination of fields.As discussed above, the dividing attribute may be a combination of userand action fields.

Anomaly detection system 902 may divide (e.g., via dataset processingsubsystem 914), based on the dividing attribute, the dataset into aplurality of datasets. For example, the anomaly detection system mayaccess the dataset and determine unique values in a field correspondingto the dividing attribute. Anomaly detection system 902 may thengenerate a data structure for each unique value and copy entriesassociated with each unique value to the corresponding data structure.For example, field 1406 of FIG. 14 may be the dividing attribute.Anomaly detection system may iterate through field 1406 to determineunique users in the dataset and generate one data structure per uniqueuser. The anomaly detection system may then iterate through the datasetand identify a user in each entry, then copy that entry to theappropriate data structure based on the user.

In some embodiments, the anomaly detection system may perform a singleiteration through the dataset to divide the dataset into multipledatasets. The anomaly detection system may iterate through each entryand determine if the value in the field corresponding to the dividingattribute was encountered in a previous entry. If it has beenencountered, the anomaly detection system may determine the appropriatedata structure for the entry, and may copy the entry into that datastructure. If the value has not been encountered before, the anomalydetection system may generate a new data structure for entries havingthat value.

In some embodiments, the dividing attribute may be multiple fields, thusthe anomaly detection system may generate one data structure for aunique combination of the values in the dividing fields. For example,for a portion of the dataset illustrated in FIG. 14, there may be twodata structures. One may correspond to the combination of User1-System1values and the other may correspond to User2-System2 values. To generatethe datasets, the anomaly detection system may iterate through eachentry and determine if the combination of values in the fieldscorresponding to the dividing attribute was encountered in a previousentry. If it has been encountered, the anomaly detection system maydetermine the appropriate data structure for the entry, and may copy theentry into that data structure. If the combination of values has notbeen encountered before, the anomaly detection system may generate a newdata structure for entries having that combination of values.

When each dataset is ready, anomaly detection system 902 may aggregate,based on a time interval, the plurality of datasets into a plurality oftimeseries datasets. Each anomaly timeseries dataset may include aplurality of timestamps. In some embodiments, dataset processingsubsystem 914 may perform the following operations to perform theaggregation of each timeseries dataset. Dataset processing subsystem 914may retrieve the time interval. For example, the time interval may beone minute, one hour, one day, or another suitable time interval.Dataset processing subsystem 914 may retrieve the time interval frommemory or from another suitable location.

Furthermore, dataset processing subsystem 914 may retrieve, from thechronologically ordered dataset, a time associated with a firsttimestamp stored in a first position within the chronologically ordereddataset. For example, dataset processing subsystem 914 may access a datastructure that stores the chronologically ordered dataset and retrievethe earliest entry (i.e., the first entry) in the dataset. In someembodiments, dataset processing subsystem 914 may determine a timeslotassociated with the first timestamp. For example, if the time intervalis one hour and the timestamp is 2021-01-01 01:11:00, dataset processingsubsystem 914 may determine that the timeslot for the entry is between 1PM and 2 PM on Jan. 1, 2021. The determination may be performed byadding the time interval to the timestamp and rounding down to thenearest period (e.g., nearest hour) and subtracting the time intervalfrom the timestamp and rounding up the timestamp to the nearest period(e.g., nearest hour).

Dataset processing subsystem 914 may then traverse the chronologicallyordered dataset until a second timestamp is reached which is the lasttimestamp within a timeslot associated with the first timestamp. Forexample, dataset processing subsystem 914 may iterate through eachtimestamp and compare each timestamp with the timeslot ending time. Theprocess may proceed until a timestamp is after the ending time and thenstop.

Dataset processing subsystem 914 may generate an aggregated value basedon all the timestamps starting from the first timestamp and ending witha last timestamp prior to the second timestamp such that the aggregatedvalue represents a count of anomalies detected starting with the firsttimestamp and ending with the second timestamp. For example, datasetprocessing subsystem 914 may add all the timestamps to arrive at theaggregated value for the specific timeslot.

As discussed above, dataset processing subsystem 914 may aggregate thedata based on an hourly interval. FIG. 11 illustrates table 1100 thatincludes timeslots and corresponding number of anomalies. Column 1102includes timeslots 1106 and 1108, while column 1104 includes a number ofanomalies detected in those timeslots.

Dataset processing subsystem 914 may input the plurality of timeseriesdatasets into a plurality of anomaly detection models to obtain aplurality of sets of anomalies. In some embodiments, the anomalydetection model may be a model as illustrated by FIG. 6 and theaccompanying disclosure. For example, the output of the anomalydetection model may be timestamps or timeslots and for each aprobability or a score that the particular timestamp or timeslot isassociated with a value indicating an anomaly. For each timeseriesdataset there may be multiple anomalies, or no anomalies detected. Insome embodiments, dataset processing subsystem 914 may determine whichtimestamps are associated with anomalies based on a thresholdprobability or score value. That is, if the associated probability orscore is higher than the threshold, dataset processing subsystem 914 mayidentify a particular timestamp as an anomaly. For example, datasetprocessing subsystem 914 may receive, from the anomaly detection modeland based on the anomaly timeseries dataset, one or more probabilitiescorresponding to the one or more anomalies detected by the anomalydetection model, and retrieve an anomaly confidence threshold. Theanomaly confidence value may be a threshold probability or a thresholdscore that determines whether a given probability corresponds to apositive detection of an anomaly.

When the anomalies are received, dataset processing subsystem 914 maygenerate an anomaly timeseries dataset from the plurality of sets ofanomalies. For example, the data processing subsystem may generate adata structure for the anomaly timeseries dataset and store, in thatdata structure, a first set of the plurality of sets of timestamps inchronological order. The dataset processing subsystem may then iteratethrough each set of anomalies and select each set in parallel, orsequentially, and place each timestamp from each selected set ofanomalies into the data structure in the chronological order to generatea chronologically ordered dataset. For example, the dataset processingsubsystem may select each timestamp in each set and iterate through thedata structure until the proper chronological place, based on thetimestamp, is located within the data structure. The data processingsubsystem may then insert the timestamp into the data structure.

In some embodiments, the anomaly timeseries dataset may be aggregatedbased on an aggregation time interval. Dataset processing subsystem 914may retrieve the aggregation time interval. For example, the aggregationtime interval may be one minute, one hour, one day, or another suitabletime interval. Dataset processing subsystem 914 may retrieve theaggregation time interval from memory or from another suitable location.

Furthermore, dataset processing subsystem 914 may retrieve, from thechronologically ordered dataset, a time associated with a firsttimestamp stored in a first position within the chronologically ordereddataset. For example, dataset processing subsystem 914 may access a datastructure that stores the chronologically ordered dataset and retrievethe earliest entry (i.e., the first entry) in the dataset. In someembodiments, dataset processing subsystem 914 may determine a timeslotassociated with the first timestamp. For example, if the aggregationtime interval is one hour and the timestamp is 2021-01-01 01:11:00,dataset processing subsystem 914 may determine that the timeslot for theentry is between 1 PM and 2 PM on Jan. 1, 2021. The determination may beperformed adding the aggregation time interval to the timestamp androunding down to the nearest interval (e.g., nearest hour) andsubtracting the aggregation time interval from the timestamp androunding up the timestamp to the nearest interval (e.g., nearest hour).

Dataset processing subsystem 914 may then traverse the chronologicallyordered dataset until a second timestamp is reached. The secondtimestamp may be the last timestamp within a timeslot associated withthe first timestamp. For example, dataset processing subsystem 914 mayiterate through each timestamp and compare each timestamp with thetimeslot ending time. The process may proceed until a timestamp is afterthe ending time is reached and then stop.

Dataset processing subsystem 914 may generate an aggregated value basedon all the timestamps starting from the first timestamp and ending witha last timestamp prior to the second timestamp. The aggregated value mayrepresent a count of anomalies detected starting with the firsttimestamp and ending with the second timestamp. For example, datasetprocessing subsystem 914 may add all the timestamps to arrive at theaggregated value for the specific timeslot.

As discussed above, dataset processing subsystem 914 may aggregate thedata based on an hourly interval. FIG. 11 illustrates table 1100 whichincludes timeslots and corresponding number of anomalies. Column 1102includes timeslots 1106 and 1108, while column 1104 includes a number ofanomalies detected in those timeslots.

Dataset processing subsystem 914 may input the anomaly timeseriesdataset into an anomaly detection model to obtain one or more anomalies.In some embodiments, the anomaly detection model may be a model asillustrated by FIG. 6 and the accompanying disclosure. For example, theoutput of the anomaly detection model may be timestamps or timeslots andfor each a probability or a score that the particular timestamp ortimeslot is associated with a value indicating an anomaly. For eachtimeseries dataset there may be multiple anomalies, or no anomaliesdetected. In some embodiments, dataset processing subsystem 914 maydetermine which timestamps are associated with anomalies based on athreshold probability or score value. That is, if the associatedprobability or score is higher than the threshold, dataset processingsubsystem 914 may identify a particular timestamp as an anomaly. Forexample, dataset processing subsystem 914 may receive, from the anomalydetection model and based on the anomaly timeseries dataset, one or moreprobabilities corresponding to the one or more anomalies detected by theanomaly detection model, and retrieve an anomaly confidence threshold.The anomaly confidence value may be a threshold probability or athreshold score that determines whether a given probability correspondsto a positive detection of an anomaly.

Dataset processing subsystem 914 may remove from the one or moreanomalies those anomalies that do not meet the anomaly confidencethreshold.

Alerting subsystem 918 may generate one or more alerts based on the oneor more anomalies, and transmit the one or more alerts to an alertprocessing system. For example, alerting subsystem 918 may generate onealert for each detected anomaly. The alert may include timeseries dataassociated with the timestamp for which the anomaly was detected. Insome embodiments, alerting subsystem 918 may generate one alert for allthe detected anomalies and include the timeseries data associated witheach timestamp. When the alert or alerts are generated, alertingsubsystem 918 may pass the alert or alerts to communication subsystem912. Communication subsystem 912 may transmit (e.g., via network 950)the alert or alerts to an appropriate alert processing system (e.g.,alert processing system 106 a).

FIG. 15 illustrates an exemplary process 1500 for improving detection ofanomalous activity, in accordance with one or more embodiments of thisdisclosure. At 1502, anomaly detection system 902 receives a request todetect anomalous activity. The request may include a data attribute.Anomaly detection system 902 may receive the request from a client (notshown) or from another source (e.g., data node 904 or alert processingsystem 906 a). At 1504, anomaly detection system 902 receives a datasetthat includes event data for a plurality of events. The event data mayinclude a plurality of fields including a timestamp field and aplurality of attribute fields. Anomaly detection system 902 may receivethe dataset from a client (not shown) or from another source (e.g., datanode 904 or alert processing system 906 a).

At 1506, anomaly detection system 902 compares the data attribute witheach field of the plurality of attribute fields. For example, theanomaly detection system may use one or more processors to perform thecomparison. At 1508, anomaly detection system 902 determines, based onthe comparing, a dividing attribute for the dataset. For example, theanomaly detection system may use one or more processors to perform thedetermination.

At 1510, anomaly detection system 902 divides, based on the dividingattribute, the dataset into a plurality of datasets. For example, theanomaly detection system may use one or more processors to perform thedivision, and store the resulting datasets in memory and/or otherstorage. At 1512, anomaly detection system 902 aggregates, based on atime interval, the plurality of datasets into a plurality of timeseriesdatasets. The anomaly detection system may perform the aggregation usingone or more processors and store the resulting datasets in memory and/orother storage. At 1514, anomaly detection system 902, inputs theplurality of timeseries datasets into a plurality of anomaly detectionmodels to obtain a plurality of sets of anomalies. The anomaly detectionsystem may perform the input using one or more processors and store theresulting output in memory and/or other storage.

At 1516, anomaly detection system 902 generates an anomaly timeseriesdataset from the plurality of sets of anomalies. The anomaly detectionsystem may perform the generation using one or more processors and maystore the resulting anomaly timeseries dataset in memory and/or storage.At 1518, anomaly detection system 902 inputs the anomaly timeseriesdataset into an anomaly detection model to obtain one or more anomalies.The anomaly detection system may perform the input using one or moreprocessors and store the resulting output in memory and/or otherstorage.

At 1520, anomaly detection system 902 generates one or more alerts basedon the one or more anomalies. The anomaly detection system may generatethe alerts using one or more processors and store the alerts in memoryand/or other storage. At 1522, anomaly detection system 902 transmitsthe one or more alerts to an alert processing system. For example, theanomaly detection system may transmit the alerts to one or more alterprocessing systems 906 a-906 n.

FIG. 16 illustrates another exemplary process 1600 for improvingdetection of anomalous activity, in accordance with one or moreembodiments of this disclosure. At 1602, anomaly detection system 902receives a request to detect anomalous activity. The request may includea data attribute. Anomaly detection system 902 may receive the requestfrom a client (not shown) or from another source (e.g., data node 904 oralert processing system 906 a). At 1604, anomaly detection system 902receives a dataset that includes event data for a plurality of events.Anomaly detection system 902 may receive the dataset from a client (notshown) or from another source (e.g., data node 904 or alert processingsystem 906 a).

At 1606, anomaly detection system 902 determines a field of theplurality of fields that matches the data attribute. For example, theanomaly detection system may use one or more processors to perform thedetermination. At 1608, anomaly detection system 902 divides, based onthe field, the dataset into a plurality of datasets. For example, theanomaly detection system may use one or more processors to perform thedivision, and store the resulting datasets in memory and/or otherstorage. At 1610, anomaly detection system 902 aggregates, based on atime interval, the plurality of datasets into a plurality of timeseriesdatasets. The anomaly detection system may perform the aggregation usingone or more processors and store the resulting datasets in memory and/orother storage. At 1612, anomaly detection system 902, inputs theplurality of timeseries datasets into a plurality of anomaly detectionmodels to obtain a plurality of sets of anomalies. The anomaly detectionsystem may perform the input using one or more processors and store theresulting output in memory and/or other storage.

At 1614, anomaly detection system 902 generates an anomaly timeseriesdataset from the plurality of sets of anomalies. The anomaly detectionsystem may perform the generation using one or more processors and maystore the resulting anomaly timeseries dataset in memory and/or storage.At 1616, anomaly detection system 902 inputs the anomaly timeseriesdataset into an anomaly detection model to obtain one or more anomalies.The anomaly detection system may perform the input using one or moreprocessors and store the resulting output in memory and/or otherstorage.

At 1618, anomaly detection system 902 generates one or more alerts basedon the one or more anomalies. The anomaly detection system may generatethe alerts using one or more processors and store the alerts in memoryand/or other storage. At 1620, anomaly detection system 902 transmitsthe one or more alerts to an alert processing system. For example, theanomaly detection system may transmit the alerts to one or more alertprocessing systems 906 a-906 n.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A method comprising: receiving a request to detect anomalousactivity, the request comprising a data attribute; receiving a datasetcomprising event data for a plurality of events, wherein the event datacomprises a plurality of fields; determining a field of the plurality offields that matches the data attribute; dividing, based the field, thedataset into a plurality of datasets; aggregating, based on a timeinterval, the plurality of datasets into a plurality of timeseriesdatasets; inputting the plurality of timeseries datasets into one ormore anomaly detection models to obtain one or more sets of anomalies;generating an anomaly timeseries dataset from the one or more sets ofanomalies; inputting the anomaly timeseries dataset into an anomalydetection model to obtain one or more anomalies; generating one or morealerts based on the one or more anomalies; and transmitting the one ormore alerts to an alert processing system.

2. The method of any of the preceding embodiments, wherein a pluralityof fields includes a timestamp field, a value field, and a plurality ofattribute fields.

3. The method of any of the preceding embodiments, wherein determiningthe field of the plurality of fields that matches the data attributecomprises: comparing the data attribute with each field of the pluralityof fields; and determining, based on the comparing, the field of theplurality fields.

4. The method of any of the preceding embodiments, wherein the pluralityof datasets includes a first dataset comprising a first type of data anda second dataset comprising a second type of data.

5. The method of any of the preceding embodiments, wherein inputtingeach of the plurality of datasets into the one or more anomaly detectionmodels comprises: selecting, based on the first type of data, a firstanomaly detection model for the first dataset; selecting, based on thesecond type of data, a second anomaly detection model for the seconddataset; and inputting the first dataset into the first anomalydetection model and inputting the second dataset into the second anomalydetection model.

6. The method of any of the preceding embodiments, wherein generatingthe anomaly timeseries dataset from the plurality of sets of anomaliescomprises: storing, in a data structure, a first set of the plurality ofsets of timestamps in a chronological order; selecting, each set of theplurality of sets of anomalies; and placing each timestamp from eachselected set of anomalies into the data structure in the chronologicalorder to generate a chronologically ordered dataset.

7. The method of any of the preceding embodiments, wherein generatingthe anomaly timeseries dataset from the plurality of sets of anomaliescomprises: retrieving an aggregation time interval; retrieving, from thechronologically ordered dataset, a time associated with a firsttimestamp stored in a first position within the chronologically ordereddataset; traversing the chronologically ordered dataset until a secondtimestamp is reached wherein the second timestamp is the last timestampwithin a timeslot associated with the first timestamp; and generating anaggregated value based on all the timestamps starting from the firsttimestamp and ending with the second timestamp, wherein the aggregatedvalue represents a count of anomalies detected starting with the firsttimestamp and ending with the second timestamp.

8. The method of any of the preceding embodiments, further comprising:receiving, from the anomaly detection model and based on the anomalytimeseries dataset, one or more probabilities corresponding to the oneor more anomalies detected by the anomaly detection model; retrieving ananomaly confidence threshold; and removing from the one or moreanomalies those anomalies that do not meet the anomaly confidencethreshold.

9. A tangible, non-transitory, machine-readable medium storinginstructions that, when executed by a data processing apparatus, causethe data processing apparatus to perform operations comprising any ofthose in embodiments 1-8.

10. A system comprising: one or more processors; and memory storinginstructions that, when executed by the processors, cause the processorsto effectuate operations comprising any of those in embodiments 1-8.

11. A system comprising means for performing any of embodiments 1-8.

12. A system comprising cloud-based circuitry for performing any ofembodiments 1-8.

Computing Environment

FIG. 17 shows an example computing system that may be used in accordancewith some embodiments. In some instances, computing system 1700 isreferred to as a computer system. A person skilled in the art wouldunderstand that those terms may be used interchangeably. The componentsof FIG. 17 may be used to perform some or all operations discussed inrelation with FIGS. 1-16. For example, operations discussed in relationto FIGS. 7-8, 12-13, and 15-16 may be performed by processors 1710a-1710 n illustrated in FIG. 17 and results stored in system memory1720. Furthermore, various portions of systems and methods describedherein may include or be executed on one or more computer systemssimilar to computing system 1700. Further, processes and modulesdescribed herein may be executed by one or more processing systemssimilar to that of computing system 1700.

Computing system 1700 may include one or more processors (e.g.,processors 1710 a-1710 n) coupled to system memory 1720, an input/outputI/O device interface 1730, and a network interface 1740 via aninput/output (I/O) interface 1750. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingsystem 1700. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may receive instructions and data from a memory (e.g., systemmemory 1720). Computing system 1700 may be a uni-processor systemincluding one processor (e.g., processor 1710 a), or a multi-processorsystem including any number of suitable processors (e.g., 1710 a-1710n). Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus can also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computing system 1700may include a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1730 may provide an interface for connection of oneor more I/O devices 1760 to computer system 1700. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1760 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1760 may be connected to computer system 1700through a wired or wireless connection. I/O devices 1760 may beconnected to computer system 1700 from a remote location. I/O devices1760 located on remote computer system, for example, may be connected tocomputer system 1700 via a network and network interface 1740.

Network interface 1740 may include a network adapter that provides forconnection of computer system 1700 to a network. Network interface 1740may facilitate data exchange between computer system 1700 and otherdevices connected to the network. Network interface 1740 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area network (WAN), a cellular communications network, or thelike.

System memory 1720 may be configured to store program instructions 1770or data 1780. Program instructions 1770 may be executable by a processor(e.g., one or more of processors 1710 a-1710 n) to implement one or moreembodiments of the present techniques. Instructions 1770 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, or a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1720 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1720 may include a non-transitory computer readablestorage medium that may have program instructions stored thereon thatare executable by a computer processor (e.g., one or more of processors1710 a-1710 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., system memory 1720) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices).

I/O interface 1750 may be configured to coordinate I/O traffic betweenprocessors 1710 a-1710 n, system memory 1720, network interface 1740,I/O devices 1760, and/or other peripheral devices. I/O interface 1750may perform protocol, timing, or other data transformations to convertdata signals from one component (e.g., system memory 1720) into a formatsuitable for use by another component (e.g., processors 1710 a-1710 n).I/O interface 1750 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1700 or multiple computer systems1700 configured to host different portions or instances of embodiments.Multiple computer systems 1700 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1700 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1700 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1700 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a Global Positioning System(GPS), or the like. Computer system 1700 may also be connected to otherdevices that are not illustrated, or may operate as a stand-alonesystem. In addition, the functionality provided by the illustratedcomponents may in some embodiments be combined in fewer components ordistributed in additional components. Similarly, in some embodiments,the functionality of some of the illustrated components may not beprovided or other additional functionality may be available.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1700 may be transmitted to computer system1700 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network or a wireless link. Various embodiments may furtherinclude receiving, sending, or storing instructions or data implementedin accordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present disclosure may be practiced with othercomputer system configurations.

Although the present invention has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred embodiments, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed embodiments, but, on the contrary, is intendedto cover modifications and equivalent arrangements that are within thescope of the appended claims. For example, it is to be understood thatthe present invention contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

The above-described embodiments of the present disclosure are presentedfor purposes of illustration and not of limitation, and the presentdisclosure is limited only by the claims which follow. Furthermore, itshould be noted that the features and limitations described in any oneembodiment may be applied to any other embodiment herein, and flowchartsor examples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real time. It should also be noted that the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

1. A system for correlating events based on anomalies occurring within agiven time interval across multiple timeseries datasets, the systemcomprising: one or more processors; and a non-transitorycomputer-readable storage medium storing instructions, which whenexecuted by the one or more processors cause the one or more processorsto: receive, from a plurality of machine learning models trained todetect the anomalies within different types of data, a plurality of setsof timestamps, wherein each timestamp within each set represents ananomaly detected within a corresponding timeseries dataset of aplurality of timeseries datasets, and wherein a time period of eachtimeseries dataset matches the time period of each other dataset in theplurality of timeseries datasets; combine timestamps representing theanomalies within the plurality of sets of timestamps into an anomalydataset; sort the anomaly dataset into a chronologically ordereddataset; transform, based on a time interval using a transformationfunction, the chronologically ordered dataset into an anomaly timeseriesdataset, wherein the anomaly timeseries dataset comprises an aggregatedvalue representing timestamps for each time interval of the time period;input the anomaly timeseries dataset into a second machine learningmodel trained to detect the anomalies within anomaly timeseries datasetsto obtain one or more anomalies from the second machine learning model;generate one or more alerts based on the one or more anomalies; andtransmit the one or more alerts to an alert processing system.
 2. Thesystem of claim 1, further comprising instructions that cause the one ormore processors to: receive the plurality of timeseries datasets,wherein each timeseries dataset of the plurality of timeseries datasetscomprises a plurality of values for a plurality of timestamps, andwherein the plurality of timeseries datasets includes a first datasetcomprising a first type of data and a second dataset comprising a secondtype of data; and input each of the plurality of timeseries datasetsinto one or more anomaly detection models to obtain the plurality ofsets of timestamps, wherein each set of the plurality of sets oftimestamps comprises one or more timestamps representing the one or moreanomalies detected within the corresponding timeseries dataset.
 3. Thesystem of claim 2, wherein the instructions that cause the one or moreprocessors to input each of the plurality of timeseries datasets intothe one or more anomaly detection models further cause the one or moreprocessors to: select, based on the first type of data, a first anomalydetection model for the first dataset; select, based on the second typeof data, a second anomaly detection model for the second dataset; andinput the first dataset into the first anomaly detection model and inputthe second dataset into the second anomaly detection model.
 4. Thesystem of claim 1, wherein the instructions that cause the one or moreprocessors to transform the chronologically ordered dataset into theanomaly timeseries dataset further cause the one or more processors to:retrieve the time interval; retrieve, from the chronologically ordereddataset, a time associated with a first timestamp stored in a firstposition within the chronologically ordered dataset; traverse thechronologically ordered dataset until a second timestamp is reached,wherein the second timestamp is a last timestamp within a time slotassociated with the first timestamp; and generating first aggregatedvalue based on all the timestamps starting from the first timestamp andending with the second timestamp, wherein the aggregated valuerepresents a count of the anomalies detected starting with the firsttimestamp and ending with the second timestamp.
 5. The system of claim1, further comprising instructions that cause the one or more processorsto: receive, from the second machine learning model and based on theanomaly timeseries dataset, one or more probabilities corresponding tothe one or more anomalies detected by the second machine learning model;retrieve an anomaly confidence threshold; and remove from the one ormore anomalies those anomalies that do not meet the anomaly confidencethreshold.
 6. A method comprising: receiving a plurality of sets oftimestamps, wherein each set of the plurality of sets of timestampsincludes a plurality of timestamps representing, each timestamprepresenting and anomaly detected within a corresponding timeseriesdataset, and wherein a time period for each set of the plurality of setsof timestamps matches the time period for each other set of theplurality of sets of timestamps; combining timestamps within theplurality of sets of timestamps into a chronologically ordered datasetof anomalies; transforming, based on a time interval using atransformation function, the chronologically ordered dataset into ananomaly timeseries dataset having an aggregated value representingtimestamps for each time interval of the time period; inputting theanomaly timeseries dataset into a machine learning model trained todetect the anomalies in anomaly timeseries datasets to obtain one ormore anomalies; generating one or more alerts based on the one or moreanomalies; and transmitting the one or more alerts to an alertprocessing system.
 7. The method of claim 6, further comprising:receiving a plurality of timeseries datasets, wherein each timeseriesdataset comprises a plurality of values; and inputting each of theplurality of timeseries datasets into one or more anomaly detectionmodels.
 8. The method of claim 7, wherein the plurality of timeseriesdatasets includes a first dataset comprising a first type of data and asecond dataset comprising a second type of data.
 9. The method of claim8, wherein inputting each of the plurality of timeseries datasets intothe one or more anomaly detection models comprises: selecting, based onthe first type of data, a first anomaly detection model for the firstdataset; selecting, based on the second type of data, a second anomalydetection model for the second dataset; and inputting the first datasetinto the first anomaly detection model and inputting the second datasetinto the second anomaly detection model.
 10. The method of claim 6,wherein combining the timestamps within the plurality of sets oftimestamps into the chronologically ordered dataset of the anomaliescomprises: storing, in a data structure, a first set of the plurality ofsets of timestamps in a chronological order; selecting, each set of theplurality of sets of timestamps; and placing each timestamp from eachselected set into the data structure in the chronological order.
 11. Themethod of claim 10, wherein transforming the plurality of sets oftimestamps into the anomaly timeseries dataset comprises: retrieving thetime interval; retrieving, from the data structure, a time associatedwith a first timestamp stored in a first position within the datastructure; traversing the data structure until a second timestamp isreached, wherein the second timestamp is a last timestamp within a timeslot associated with the first timestamp; and generating a firstaggregated value based on all the timestamps between the first timestampand the second timestamp, wherein the aggregated value represents acount of the anomalies detected between the first timestamp and thesecond timestamp.
 12. The method of claim 6, further comprising:receiving, from the machine learning model and based on the anomalytimeseries dataset, one or more probabilities corresponding to the oneor more anomalies detected by the machine learning model; retrieving ananomaly confidence threshold; and removing from the one or moreanomalies those anomalies that do not meet the anomaly confidencethreshold.
 13. A non-transitory, computer-readable medium forcorrelating events based on anomalies occurring within a given timeinterval across multiple timeseries datasets, storing instructions that,when executed by one or more processors, cause operations comprising:receiving a plurality of sets of timestamps, wherein each set of theplurality of sets of timestamps includes one or more timestampsrepresenting, each timestamp representing an anomaly detected within acorresponding timeseries dataset, and wherein a time period for each setof the plurality of sets of timestamps matches the time period for eachother set of the plurality of sets of timestamps; combining timestampswithin the plurality of sets of timestamps into a chronologicallyordered dataset of the anomalies; transforming, based on a time intervalusing a transformation function, the chronologically ordered datasetinto an anomaly timeseries dataset having an aggregated valuerepresenting timestamps for each time interval of the time period;inputting the anomaly timeseries dataset into a machine learning modeltrained to detect the anomalies in anomaly timeseries datasets to obtainone or more anomalies; generating one or more alerts based on the one ormore anomalies; and transmitting the one or more alerts to an alertprocessing system.
 14. The non-transitory, computer-readable medium ofclaim 13, wherein the instructions further cause one or more processorsto: receive a plurality of timeseries datasets, wherein each timeseriesdataset comprises a plurality of values for a plurality of timestamps;and input each of the plurality of timeseries datasets into one or moreanomaly detection models.
 15. The non-transitory, computer-readablemedium of claim 14, wherein the plurality of timeseries datasetsincludes a first dataset comprising a first type of data and a seconddataset comprising a second type of data.
 16. The non-transitory,computer-readable medium of claim 15, wherein the instructions thatcause the one or more processors to input each of the plurality oftimeseries datasets into the one or more anomaly detection modelsfurther cause the one or more processors to: select, based on the firsttype of data, a first anomaly detection model for the first dataset;select, based on the second type of data, a second anomaly detectionmodel for the second dataset; and input the first dataset into the firstanomaly detection model and inputting the second dataset into the secondanomaly detection model.
 17. The non-transitory, computer-readablemedium of claim 13, wherein the instructions that cause the one or moreprocessors to combine the timestamps within the plurality of sets oftimestamps into the chronologically ordered dataset of the anomaliesfurther cause the one or more processors to: store, in a data structure,a first set of the plurality of sets of timestamps in a chronologicalorder; select, each set of the plurality of sets of timestamps; andplace each timestamp from each selected set into the data structure inthe chronological order.
 18. The non-transitory, computer-readablemedium of claim 17, wherein the instructions that cause the one or moreprocessors to transform the plurality of sets of timestamps into theanomaly timeseries dataset further cause the one or more processors to:retrieve the time interval; retrieve, from the data structure, a timeassociated with a first timestamp stored in a first position within thedata structure; traverse the data structure until a second timestamp isreached, wherein the second timestamp is a last timestamp within a timeslot associated with the first timestamp; and generate a firstaggregated value based on all the timestamps between the first timestampand the second timestamp, wherein the aggregated value represents acount of the anomalies detected between the first timestamp and thesecond timestamp.
 19. The non-transitory, computer-readable medium ofclaim 13, wherein the instructions further cause the one or moreprocessors to: receive, from the machine learning model and based on theanomaly timeseries dataset, one or more probabilities corresponding tothe one or more anomalies detected by the machine learning model;retrieve an anomaly confidence threshold; and remove from the one ormore anomalies those anomalies that do not meet the anomaly confidencethreshold.
 20. The non-transitory, computer-readable medium of claim 13,wherein the instructions that cause the one or more processors totransform the plurality of sets of timestamps into the anomalytimeseries dataset further cause the one or more processors to generatethe anomaly timeseries dataset with a plurality of time windows and acorresponding number of anomalies detected during a corresponding timewindow.